Abstract
More and more clinical observations have implied that microbes have great effects on human diseases. Understanding the relations between microbes and diseases are of profound significance for disease prevention and therapy. In this paper, we propose a predictive model based on the known microbe-disease associations to discover potential microbe-disease associations through integrating Learning Graph Representations and a modified Scoring mechanism on the Heterogeneous network (called LGRSH). Firstly, the similarity networks for microbe and disease are obtained based on the similarity of Gaussian interaction profile kernel. Then, we construct a heterogeneous network including these two similarity networks and microbe-disease associations’ network. After that, the embedding algorithm Node2vec is implemented to learn representations of nodes in the heterogeneous network. Finally, according to these low-dimensional vector representations, we calculate the relevance between each microbe and disease by utilizing a modified rule-based inference method. By comparison with three other methods including LRLSHMDA, KATZHMDA and BiRWHMDA, LGRSH performs better than others. Moreover, in case studies of asthma, Chronic Obstructive Pulmonary Disease and Inflammatory Bowel Disease, there are 8, 8, and 10 out of the top-10 discovered disease-related microbes were validated respectively, demonstrating that LGRSH performs well in predicting potential microbe-disease associations.
Highlights
Varieties of microbial communities are dominant throughout the human different body niches including skin, mouth, respiratory tract, throat, stomach, gut and colon, which mainly compose of bacteria, protozoa, archaeon, viruses, and fungi (Methe et al, 2012; Althani et al, 2016)
By using the Gaussian interaction profile (GIP) kernel similarity, Chen et al (2017) developed a prediction method called KATZHMDA that infers potential associations based on the number and length of walks in a heterogeneous network
Case studies of asthma, Chronic Obstructive Pulmonary Disease (COPD) and inflammatory bowel disease (IBD) demonstrate that LGRSH can be considered as an effective method for association prediction
Summary
Varieties of microbial communities are dominant throughout the human different body niches including skin, mouth, respiratory tract, throat, stomach, gut and colon, which mainly compose of bacteria, protozoa, archaeon, viruses, and fungi (Methe et al, 2012; Althani et al, 2016). The Human Microbiome Project Consortium (HMP) was funded to explore the relationships between microbes and human diseases It generates a wide range of quality-controlled resources and data to develop metagenomic protocols, which is available for scientific research (Methe et al, 2012). By using the Gaussian interaction profile (GIP) kernel similarity, Chen et al (2017) developed a prediction method called KATZHMDA that infers potential associations based on the number and length of walks in a heterogeneous network. Fan et al (2019) proposed a method called MDPH_HMDA for prediction by executing standardized HeteSim measurements to weight the relations in a heterogeneous network combined by the GIP kernel similarity, the microbe–microbe functional similarity and the symptombased human disease similarity. Case studies of asthma, Chronic Obstructive Pulmonary Disease (COPD) and IBD demonstrate that LGRSH can be considered as an effective method for association prediction
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
